Due to the rapid development of artificial intelligence (AI) and machine learning (ML), an increasing number of medical devices and clinical decision support software now rely on AI/ML algorithms.
The current regulatory policies of the Center of Diagnostics and Radiologic Health (CDRH) at the US Food and Drug Administration (FDA) require algorithms within such systems to be locked, i.e. the algorithms cannot change post-approval.
However, the performance of locked prediction algorithms can degrade over time due to changes in clinical practice patterns, shifts in the patient population, and more [1]}, [2]}.
Thus, there is growing interest in deploying continuously evolving ML systems that learn from data generated during healthcare delivery [3]}, [4]}, [5]}, [6]}, and has long been the object of study in a number of fields, including continual/lifelong learning [7]}, online learning [8]}, meta-learning [9]}, and adaptive analyses [10]}.
In contrast to locked systems, continually evolving systems have the potential to adapt to distributional shifts, better reflect real-world settings, and incorporate new advancements in ML [11]}.
Because evolving algorithms present many new regulatory challenges, the FDA recently proposed a new framework for approving modifications to AI/ML-based Software as a Medical Device (SaMD) in a discussion paper [12]}.
